Walking the Cattle Continuum: Moving From the BovineSNP50 to Higher- and Lower-Density SNP Panels - PowerPoint PPT Presentation

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Walking the Cattle Continuum: Moving From the BovineSNP50 to Higher- and Lower-Density SNP Panels. Introduction. The Illumina Bovine SNP50 Bead Chip has been very successful A new high-density chip with 778K markers is now available A low-density chip with 3K markers will be available soon

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Walking the Cattle Continuum: Moving From the BovineSNP50 to Higher- and Lower-Density SNP Panels

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Walking the cattle continuum moving from the bovinesnp50 to higher and lower density snp panels l.jpg

Walking the Cattle Continuum: Moving From the BovineSNP50 to Higher- and Lower-Density SNP Panels


Introduction l.jpg

Introduction

  • The Illumina Bovine SNP50 Bead Chip has been very successful

  • A new high-density chip with 778K markers is now available

  • A low-density chip with 3K markers will be available soon

  • Other densities under development


Bovine snp50 bead chip l.jpg

Bovine SNP50 Bead Chip

  • The Illumina Bovine SNP50 Bead Chip has been very successful

  • 43,382 SNP used for genetic prediction

  • 47,645 animals genotyped in the US, many more worldwide

  • 2nd generation chip with a slightly different SNP set has been developed


Uses of the snp50 l.jpg

Uses of the SNP50

  • Genetic improvement

    • Genomic prediction

    • Parentage and breed confirmation

  • Scientific research

    • Improving the assembly

    • QTL discovery (calving traits, SCS)

      • Recessives and causative mutations

    • Phylogeny


Most holstein genotypes feb 2010 l.jpg

Most Holstein genotypes Feb 2010


Genotyped holsteins august 2010 l.jpg

Genotyped Holsteins August 2010

*Traditional evaluation

**No traditional evaluation


Rel for young holstein bulls july 2010 l.jpg

5000

4500

4000

Bulls (no.)

3500

3000

2500

2000

1500

1000

500

0

60

61

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

REL (%) for mlk yield

REL for young Holstein bulls July 2010


Bovine high density bead chip hd l.jpg

Bovine High-Density Bead Chip (HD)

  • 778K SNP chosen to

    • Be evenly spaced

    • Include some Y-specific SNP

    • Include mitochondrial SNP

  • Utilize across-breed information

  • Fine mapping of QTL

  • Enhanced performance in Zebu cattle


Collaboration was essential l.jpg

Collaboration was essential

  • University of Missouri

  • Roslin Institute

  • UNCEIA (France)

  • Sao Paulo State University

  • University of Milan

  • Technische Universitaet Muenchen

  • Beef CRC

  • Embrapa

  • National University (Korea)

  • Illumina provided:

    • DNA sequence for a range of breeds

  • Pfizer provided:

    • DNA sequence of additional breeds

    • SNP discovery expertise

  • USDA-ARSprovided:

    • DNA and library construction

    • SNP discovery expertise

    • Assay design expertise


Data highlights l.jpg

Data highlights

  • Enormous amount of DNA sequence data

    • ~180-200x genome equivalent coverage

    • ~600 BILLION base-pairs

  • Represents:

    • ~120 libraries

    • >300 animals

  • Animals from breeds representing:

    • European and Zebu cattle

    • Beef and dairy

    • Temperate and tropically adapted


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Partners

Deep SNP Discovery

N’Dama

Sahiwal

Simmental

Hanwoo

Blonde d’Aquitaine

Montbeliard

BFGL

Genome Assemblies

Nelore

Water Buffalo

BFGL-Illumina

Deep SNP Discovery

Angus

Holstein

Limousin

Jersey

Nelore

Brahman

Romagnola

Gir

Pfizer

Light SNP Discovery

Angus

Holstein

Jersey

Hereford

Charolais

Simmental

Brahman

Waygu


High density chip design l.jpg

High-density chip design

  • >45 million SNPs discovered

  • ~6 million were used to design the high density chip

    • ~800,000 new SNPs added

    • Kept almost all of the BovineSNP50 SNPs

  • Breed groups included

    • Holstein, Angus, Nelore, Taurine dairy, Taurine beef, Indicine, tropically adapted Taurine

  • 852,645 total gaps

    • 850,816 (99.8%) < 20kb

    • 1,795 >20kb, < 100kb

    • 34 > 100 kb


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The HD chip in practice

  • 777,962 available SNP

  • 160 bulls genotyped

  • 186,705 SNP edited-out

    • 1,269 unassigned chromosome

    • 3,197 low call rate

    • 1,804 Hardy-Weinberg failures

    • 115,850 MAF < 0.01

    • 64,585 uncertain location

  • 591,258 useable SNP


Bovine low density bead chip 3k l.jpg

Bovine Low-Density Bead Chip (3K)

  • 2,900 SNP

    • Evenly spaced

    • 2,882 useable SNP

    • 14 Y-specific SNP

    • Includes 82 SNP for breed determination

  • Expected to ship very soon

    • Allflex tissue-collection tags to be released

    • Canada will use DNA Genotek nasal swabs

  • Large initial use anticipated


Applications of the 3k chip l.jpg

Applications of the 3K chip

  • Producing AI sires

    • Accuracy adequate for initial screening

    • 50K or HD genotyping for bulls acquired

      • Confirm ID

      • Second-stage selection

    • Genotype more candidates for less money

  • Parentage verification and pedigree discovery

  • Traceability for disease outbreaks


Other chips l.jpg

Other chips

  • 96 SNP parentage chip

    • Use to identify and correct pedigree errors

    • Very low cost

  • 384 SNP chip

    • Use for initial screening of cows

    • 70 to 80% of benefit of 50K for 10% of cost with haplotyping and parental genotypes

  • 700K SNP Affymetrix chip

    • Will be cheaper than Illumina HD chip


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Illumina chips are [mostly] nested

Bovine HD (700K)

Missing 7,352 SNP50 markers

Missing 5,264 V2 markers

Bovine SNP50 (50K)

SNP50 v 2 (V2)

50K is missing 14 3K markers

Missing 76 3K markers

Bovine LD (3K)


How do we deal with other chips l.jpg

How do we deal with other chips?

  • Impute to highest density

  • Calculate SNP effects for all HD SNP

  • Account for loss in accuracy due to imputation error

  • Store only observed genotypes

  • Label evaluations with source of genotype


Why impute haplotypes l.jpg

Why impute haplotypes?

  • Predict unknown SNP from known

    • Measure 3,000, predict 50,000 SNP

    • Measure 50,000, predict 500,000

    • Measure each haplotype at highest density only a few times

  • Predict dam from progeny SNP

  • Increase reliabilities for less cost


How does imputation work l.jpg

How does imputation work?

  • Identify haplotypes in population using many markers

  • Track haplotypes with fewer markers

  • e.g., use 5 SNP to track 25 SNP

    • 5 SNP: 22020

    • 25 SNP: 2022020002002002000202200


Example bull haplotypes chromosome 15 l.jpg

Example bull haplotypes chromosome 15


Expected rel with haplotyping l.jpg

Expected REL with haplotyping

  • Actual 3Ksubset of 50K genotypes

    • Correlation (50K, 3K) was .95 to .97

    • REL PA = 35% , 3K = 63% , 50K = 70%

  • Simulated 500K genotypes

    • REL, all animals 50K = 82.6%, 500K = 84%

    • REL improved only if >1,000 had 500K

  • Gains in reliability above PA

    • 3K chip gives >80% of 50K REL gain

    • 50K chip gives >96% of 500K REL gain


Rel using 3k 50k or 500k snp l.jpg

REL Using 3K, 50K, or 500K SNP


Whole genome sequencing l.jpg

Whole-genome sequencing

  • Whole-genome sequences on individuals will be available in the next 5 years

    • How will we store and use those data?

  • Not feasible to calculate SNP effects for 3,000,000,000 SNP

  • Best application may be SNP identification


Other genotyping issues l.jpg

Other genotyping issues

  • Collection of genotypes from universities and public research organization

  • 3K genotypes from cooperator herds need to enter the national dataset for reliable imputation

  • Encourage even more widespread sharing of genotypes across countries

  • Funding of genotyping necessary to predict SNP effects for future chips

  • Intellectual property issues


Conclusions l.jpg

Conclusions

  • The 50K chip has been very successful, but other densities are coming

  • We are collaboratively developing tools to increase the ability to characterize cattle with both lower and higher density SNP chips

  • This technology has the potential to impact the developing world


Slide27 l.jpg

Implementation Team

iBMAC Consortium

Funding

  • Illumina (industry)

    • Marylinn Munson

    • Cindy Lawley

    • Diane Lince

    • LuAnn Glaser

    • Christian Haudenschild

  • Beltsville (USDA-ARS)

    • Curt Van Tassell

    • Lakshmi Matukumalli

    • Steve Schroeder

    • Tad Sonstegard

  • Univ Missouri (Land-Grant)

    • Jerry Taylor

    • Bob Schnabel

    • Stephanie McKay

  • Univ Alberta (University)

    • Steve Moore

  • Clay Center, NE (USDA-ARS)

    • Tim Smith

    • Mark Allan

  • AIPL

    • Paul VanRaden

    • George Wiggans

    • John Cole

    • Leigh Walton

    • Duane Norman

  • BFGL

    • Marcos de Silva

    • Tad Sonstegard

    • Curt Van Tassell

  • University of Wisconsin

    • Kent Weigel

  • University of Maryland School of Medicine

    • Jeff O’Connell

  • Partners

    • GeneSeek

    • DNA Landmarks

    • Expression Analysis

    • Genetic Visions

  • USDA/NRI/CSREES

    • 2006-35616-16697

    • 2006-35205-16888

    • 2006-35205-16701

    • 2008-35205-04687

    • 2009-65205-05635

  • USDA/ARS

    • 1265-31000-081D

    • 1265-31000-090D

    • 5438-31000-073D

  • Merial

    • Stewart Bauck

  • NAAB

    • Gordon Doak

    • Accelerated Genetics

    • ABS Global

    • Alta Genetics

    • CRI/Genex

    • Select Sires

    • Semex Alliance

    • Taurus Service

28


Questions about different chips l.jpg

Questions about different chips?


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